Explainable Machine Learning for Diabetes Complication Prediction Using SHAP and Intervention Simulation
The increasing availability of structured healthcare data has accelerated the use of Machine Learning (ML) to predict diabetes complications. However, limited interpretability remains a major barrier to clinical adoption. This study presents a unified framework that integrates predictive modeling, SHapley Additive exPl...